rm(list=ls())
set.seed(1234)
library(car)
library(plyr)
library(ggplot2)
library(psycho)
library(texreg)
library(psych)
library(effectsize)

setwd(".../Data/Raw")

modeldata<-read.csv("Brazil.csv")


##create treatment number 

modeldata$Vignettes_DO_treat1 <-recode(modeldata$Vignettes_DO_treat1, " 1 = 1; else=0")
modeldata$Vignettes_DO_treat2 <-recode(modeldata$Vignettes_DO_treat2, " 1 = 2; else=0")
modeldata$Vignettes_DO_treat3 <-recode(modeldata$Vignettes_DO_treat3, " 1 = 3; else=0")
modeldata$Vignettes_DO_treat4 <-recode(modeldata$Vignettes_DO_treat4, " 1 = 4; else=0")
modeldata$Vignettes_DO_treat5 <-recode(modeldata$Vignettes_DO_treat5, " 1 = 5; else=0")
modeldata$Vignettes_DO_treat6 <-recode(modeldata$Vignettes_DO_treat6, " 1 = 6; else=0")

modeldata$treat<-modeldata$Vignettes_DO_treat1 +  modeldata$Vignettes_DO_treat2 + modeldata$Vignettes_DO_treat3 + modeldata$Vignettes_DO_treat4 + modeldata$Vignettes_DO_treat5 + modeldata$Vignettes_DO_treat6

table(modeldata$treat)

#create substantive legitmacy and procedural legitmacy standardized scores 

modeldata$decide_citizen <-as.character(modeldata$decide_citizen)
modeldata$decide_women <-as.character(modeldata$decide_women)
modeldata$decide_animal <-as.character(modeldata$decide_animal)
modeldata$fair_women <-as.character(modeldata$fair_women)
modeldata$fair_animal <-as.character(modeldata$fair_animal)
modeldata$decisionprocess <-as.character(modeldata$decisionprocess)
modeldata$overturn <-as.character(modeldata$overturn)
modeldata$council_trust <-as.character(modeldata$council_trust)
modeldata$personalcouncil <-as.character(modeldata$personalcouncil)

#Recode values

modeldata[modeldata =="Muito injusta"]<-1
modeldata[modeldata =="Um pouco injusta"]<-2
modeldata[modeldata =="Um pouco justa"]<-3
modeldata[modeldata =="Um pouca justa"]<-3
modeldata[modeldata =="Muito justa"]<-4

modeldata[modeldata =="Muito injusto"]<-1
modeldata[modeldata =="Um pouco injusto"]<-2
modeldata[modeldata =="Um pouco justo"]<-3
modeldata[modeldata =="Muito justo"]<-4

modeldata[modeldata =="Discordo totalmente"]<-1
modeldata[modeldata =="Discordo"]<-2
modeldata[modeldata =="Concordo"]<-3
modeldata[modeldata =="Concordo totalmente"]<-4

modeldata$decide_citizen<-as.numeric(as.character(modeldata$decide_citizen))
modeldata$decide_women <-as.numeric(as.character(modeldata$decide_women))
modeldata$decide_animal <-as.numeric(as.character(modeldata$decide_animal))
modeldata$fair_women <-as.numeric(as.character(modeldata$fair_women))
modeldata$fair_animal <-as.numeric(as.character(modeldata$fair_animal))
modeldata$decisionprocess <-as.numeric(as.character(modeldata$decisionprocess))
modeldata$overturn <-as.numeric(as.character(modeldata$overturn))
modeldata$council_trust <-as.numeric(as.character(modeldata$council_trust))
modeldata$personalcouncil <-as.numeric(as.character(modeldata$personalcouncil))

#reverse code overturn, so higher values represent higher legitimacy views

modeldata$overturnR<-recode(modeldata$overturn, " 1=4; 2=3; 3=2; 4=1")

#subset the data for the issue area of sexual harassment 

harass<-subset(modeldata, treat == 1 | treat == 2 | treat == 3 )

##sexual harassment vignettes (treat 1 = AMP, treat 2 = GBP, treat 3 = Q-GBP)

#Create indicies and plot for three treatment groups 

#subset the data to those that pass the manipulation checks
harass<-subset(harass, harass$decide_harass=="Exigir treinamento sobre assédio sexual.")

#create the substantive legitimacy index

factor1<-omega(harass[,c(46:47, 49)], nfactors=1) 
harass$SubLeg<-factor1$scores[,1]
harass$SubLegStand<-standardize(harass$SubLeg)


factor1<-omega(harass[,c(51, 53, 68)], nfactors=1) #alpha = 0.68
harass$ProLeg<-factor1$scores[,1]
harass$ProLegStand<-standardize(harass$ProLeg)


##subset the animal mistreatment vignettes 
animal<-subset(modeldata, treat == 4 | treat == 5 | treat == 6)

#subset the data to those that pass the manipulation checks
animal <-subset(animal, decide_animal.1 == "Exigir treinamento sobre maus-tratos a animais.")

#Create indicies and plot for three treatment groups 

factor1<-omega(animal[,c(46, 48, 50)], nfactors=1) #alpha = 0.81
animal $SubLeg<-factor1$scores[,1]
animal $SubLegStand<-standardize(animal $SubLeg)


factor1<-omega(animal[,c(51, 53, 68)], nfactors=1) #alpha = 0.72
animal $ProLeg<-factor1$scores[,1]
animal $ProLegStand<-standardize(animal $ProLeg)

animal <- animal[!is.na(animal $SubLegStand),] #remove NA values from DV


#create and export model dataset 

BrazilAgg<-rbind(harass[, c(67, 70, 72, 18, 20)], animal[, c(67, 70, 72, 18, 20)]) 
BrazilAgg $Country <-rep("Brazil", nrow(BrazilAgg))

write.csv(BrazilAgg, "BrazilAgg.csv")


